\begin{abstract}
  Diffusion processes are important models for many real-world
  phenomena, such as the spread of disease or rumors. We propose to
  study different aspects of diffusion processes in networks, focusing
  on designing efficient distributed algorithms for positive diffusion
  processes and good intervention strategies to control harmful
  diffusions.

  First, we design and analyze various distributed algorithms for
  diffusion processes. We want to devise efficient distributed
  algorithms, which are easy to implement, to help the spreading of
  positive/useful information. We refer to these processes as positive
  diffusions. Earlier work has studied this for a variety of models,
  mainly based on static networks. The major point that separates our
  research with previous work is that we consider dynamically changing
  networks, which extends previous models to a larger range of
  real-life situations. Depending on the ways that networks are
  altered, we propose to study diffusion processes over the following
  two types of dynamic networks: (1) networks are changed due to
  individuals' decisions or behaviors; (2) networks are controlled by
  an adversary.

  Secondly, we study how to devise good intervention strategies to
  control diffusion processes. This problem is crucial when we deal
  with harmful information like human diseases or computer viruses. We
  refer to these processes as harmful diffusions. We distinguish
  between centralized and decentralized intervention strategies. In
  centralized intervention strategies, there is a controller who has a
  limited amount of intervention resources (e.g. vaccinations or
  antidotes in the case of diseases). We study the problem of
  allocating these limited resources among the network agents so that
  the spread of the diffusion process is minimized. In decentralized
  intervention strategies, each individual in the network makes their
  own decision on protecting themselves, based on their individual
  utility and local knowledge. In such settings, we are interested in
  questions such as: is there a stable set of intervention strategies?
  What's the cost of decentralized solutions compared with an optimal
  centralized one? Lastly, we augment our studies of intervention
  strategies with the consideration about individual behavior changes
  which would lead to a new kind of network dynamics. Earlier work has
  shown that the combination of behavior change and intervention
  failure (e.g. failed vaccination) can lead to perverse outcomes
  where less (intervention resources) is more (effective). However,
  the extent of the perversity and its dependence on network structure
  as well as the precise nature of the behavior change has remained
  largely unknown.  \junk{And that is going to be the focus of our
    research in this domain.}
\end{abstract}
